SEMgsa: SEM-based gene set analysis

In SEMgraph: Network Analysis and Causal Inference Through Structural Equation Modeling

SEM-based gene set analysis

Description

Gene Set Analysis (GSA) via self-contained test for group effect on signaling (directed) pathways based on SEM. The core of the methodology is implemented in the RICF algorithm of SEMrun(), recovering from RICF output node-specific group effect p-values, and Brown’s combined permutation p-values of node activation and inhibition.

Usage

SEMgsa(g = list(), data, group, method = "BH", alpha = 0.05, n_rep = 1000, ...)

Arguments

g	A list of pathways to be tested.
data	A matrix or data.frame. Rows correspond to subjects, and columns to graph nodes (variables).
group	A binary vector. This vector must be as long as the number of subjects. Each vector element must be 1 for cases and 0 for control subjects.
method	Multiple testing correction method. One of the values available in p.adjust. By default, method is set to "BH" (i.e., Benjamini-Hochberg correction).
alpha	Gene set test significance level (default = 0.05).
n_rep	Number of randomization replicates (default = 1000).
...	Currently ignored.

Details

For gaining more biological insights into the functional roles of pre-defined subsets of genes, node perturbation obtained from RICF fitting has been combined with up- or down-regulation of genes from a reference interactome to obtain overall pathway perturbation as follows:

• The node perturbation is defined as activated when the minimum among the p-values is positive; if negative, the status is inhibited.

• Up- or down- regulation of genes is computed from the weighted adjacency matrix of each pathway as column sum of weights(-1,0,1) over each source node. If the overall sum of node weights is below 1, the pathway is flagged as down-regulated, otherwise as up-regulated.

• The combination between these two quantities allows to define the direction (up or down) of gene perturbation. Up- or down regulated gene status, associated with node inhibition, indicates a decrease in activation (or increase in inhibition) in cases with respect to control group. Conversely, up- or down regulated gene status, associated with node activation, indicates an increase in activation (or decrease in inhibition) in cases with respect to control group.

Value

A list of 2 objects:

1. "gsa", A data.frame reporting the following information for each pathway in the input list:

   • "No.nodes", pathway size (number of nodes);

   • "No.DEGs", number of differential espression genes (DEGs) within the pathway, after multiple test correction with one of the methods available in p.adjust;

   • "pert", pathway perturbation status (see details);

   • "pNA", Brown's combined P-value of pathway node activation;

   • "pNI", Brown's combined P-value of pathway node inhibition;

   • "PVAL", Bonferroni combined P-value of pNA, and pNI; i.e., 2* min(pNA, PNI);

   • "ADJP", Adjusted Bonferroni P-value of pathway perturbation; i.e., min(No.pathways * PVAL; 1).

2. "DEG", a list with DEGs names per pathways.

Author(s)

Mario Grassi [e-mail]

References

Grassi, M., Tarantino, B. (2022). SEMgsa: topology-based pathway enrichment analysis with structural equation models. BMC Bioinformatics, 17 Aug, 23, 344. <https://doi.org/10.1186/s12859-022-04884-8>

Examples

## Not run: 

# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data

# Selection of FTD-ALS pathways from KEGG pathways

paths.name <- c("MAPK signaling pathway",
                "Protein processing in endoplasmic reticulum",
                "Endocytosis",
                "Wnt signaling pathway",
                "Neurotrophin signaling pathway",
                "Amyotrophic lateral sclerosis")

j <- which(names(kegg.pathways) %in% paths.name)

GSA <- SEMgsa(kegg.pathways[j], als.npn, alsData$group,
              method = "bonferroni", alpha = 0.05,
              n_rep = 1000)
GSA$gsa
GSA$DEG


## End(Not run)

SEMgraph documentation built on Dec. 18, 2025, 1:07 a.m.